The present invention is directed towards fitting a programmable hearing aid by providing electroacoustic targets based on hearing impairments, on the learned information from previous successful fittings and on user comments/responses while listening to different sound environments and those targets are to be matched by the response of the hearing aid.
Programmable hearing aids offer the possibility of making choices for the values of multiple parameters, hence allowing for a very wide range of electroacoustic responses capable of accommodating many different kinds of hearing impairments. The audiologist/hearing aid practitioner using audiometric measurements decides on the objectives/targets that the hearing device should match in order to compensate for the impairment.
With the advent of programmable hearing instruments, it has become possible to achieve near optimal matching between the electroacoustic responses and the targets. The targets are derived from prescriptive procedures, based on theoretical or empirical considerations, originally proposed for linear nonprogrammable hearing devices. Lately, new prescriptive formulas intended for nonlinear programmable devices such as DSLi/o are being used. Nevertheless, their targets are based on artificial listening environments (i.e., speech weighted noise) and do not adequately characterize hearing aid performance in realistic environments.
The audiologist is then often faced with user complaints that reflect the performance of the device in everyday environments and he/she then must adjust the operating parameters of the device with suboptimal tools and methods. Setting those parameters one at a time (e.g., AGC for the low channel, Gain for the high channel) is a suboptimal procedure because there is a great degree of interdependence between the parameters. Furthermore, the user responses and complaints are imprecise and uncertain and they need to be deciphered by the audiologist.
Finally, even tools that allow for an automatic mechanism for deciphering the user responses, such as the fuzzy logic device described in U.S. Pat. No. 5,606,620, are not optimal because they directly control the individual parameters of the device. Because of this direct control, many contradictory demands on setting the parameters are not adequately resolved by the inherent capabilities of the fuzzy logic. Furthermore, the said system does not provide for an efficient mechanism for incorporating learning from successful fittings other than the manual entry of fuzzy rules.
The invention provides for a neurofuzzy device that as a first step in the fitting process, generates initial targets for compensating a particular hearing loss. These targets are based both on collected individualized audiometric and other data and on the accumulated learning from previous successful fittings. Starting the fitting process with efficient initial targets could significantly shorten the process.
Targets could have the form of gain curves for different input levels, signal to noise curves, etc. These curves are not directly dependent on the particular hearing device used, rather they represent the electroacoustic response of an xe2x80x9cidealxe2x80x9d prosthesis for the particular impairment.
The targets are generated by a multilayer neural network which is a xe2x80x9cblack boxxe2x80x9d information processing system trained to generate an optimum match between a set of audiometric measurements such as the auditory thresholds and the corresponding best frequency and gain curves at different input levels for each subject. The neural network requires a priori knowledge and acquiring it requires large amounts of data in order to converge to a solution.
The required a priori knowledge is entered into the network during an off line training session performed during manufacturing using data collected from hearing aid dispensing outlets from selected geographical areas. The currently available prescriptive rationales such as NAL are based on such data and are used in fitting traditional non programmable hearing aids as well as starting frequency/gain target curves for programmable devices.
The known rationales are fairly limited in scope as it is not possible to develop formulas based on observations of large fitting data that will adequately reflect the interdependencies in the data. A neural network can far more effectively capture the essential nonlinearities of the problem. The captured knowledge is in the form of nodal weights in the hidden layers of the network.
Training the network is also an ongoing process which is enabled after the fine tuning process that culminated in a successful fitting. The modified targets, resulting from the fuzzy logic based fine tuning process described later on, are used for retraining the neural network. Such on-line training allows for the neurofuzzy fitting device to be biased toward the peculiarities of a particular clientele.
The fine-tuning process in this neurofuzzy methodology closes the loop of the fitting process. After the initial targets are generated, the settings of all the parameters of the hearing prosthesis are derived and transmitted to the hearing aid. The user is then asked to listen to different sound stimuli (e.g., speech at different levels, speech and noise at different signal to noise ratio etc.) and rate the performance of the hearing aid using qualities of sound perception such as loudness, tonality, comfort, distortion, clarity etc. The fuzzy interface modifies the targets taking as inputs the user response as well as certain objective characteristics of the sound stimuli (e.g., overall sound pressure level and signal to noise Ratio) and using preentered rules.
The preentered fuzzy rules could be provided by the manufacturer or locally by the audiologist. The new modified targets are used for the derivation of a new set of values, for all the parameters, which in turn are downloaded to the hearing aid. A new battery of tests is completed and the circle is repeated until satisfactory results are achieved.
The hearing aid parameters are a function of both the sound characteristics of the input to the hearing aid and the target curves (which, at the limit, are identical to the electroacoustic response of the hearing aid). This relationship can be encoded in a neural network by pretraining it using the targets and the sound characteristics as inputs and the corresponding parameters as outputs. At the end of the fitting process, and depending on the measure of satisfaction, the audiologist/hearing aid professional can use the final targets for the automatic retraining of the neural network that generates the initial targets.
Numerous other advantages and features of the present invention will become readily apparent from the following detailed description of the invention and the embodiments thereof, from the claims and from the accompanying drawings.